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Record W2169420257 · doi:10.1111/hdi.12170

Physical examination of arteriovenous fistula: The influence of professional experience in the detection of complications

2014· article· en· W2169420257 on OpenAlexvenueno aff
Clemente Neves Sousa, Paulo Teles, Vanessa Filipa Ferreira Dias, João Apóstolo, María Henriqueta Figueiredo, María Manuela Martins

Bibliographic record

VenueHemodialysis International · 2014
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisArteriovenous fistulaStenosisComplicationFistulaPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Vascular access is one of the leading causes of mobilization of financial resources in health systems for people with chronic kidney disease on hemodialysis. Physical examination of the arteriovenous fistula (AVF) has demonstrated its effectiveness in identifying complications. We decided to evaluate the influence of nurses' professional experience in the detection of complications of the AVF (venous stenosis and steal syndrome). The study took place in eight hemodialysis centers between May and September of 2011 in the north of Portugal. Sample was constituted by registered nurses. The nurses involved in the experiment were divided in two groups: those who had more than 5 years of experience and those who had less than 5 years of experience. Ninety-two nurses participated in the study: 34 nurses had less than 5 years of professional experience and 58 had more than 5 years of professional experience. In the practices considered by nurses in the detection of venous stenosis, there were no differences observed between the groups (P > 0.05). In steal syndrome, there were no differences observed between the groups in the practices of the nurses in the detection of this complication of the AVF (P > 0.05). We concluded that professional experience does not influence the detection of venous stenosis and steal syndrome.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.357
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2014
Admission routes1
Has abstractyes

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